Cellular networks have traditionally been deployed in a homogenous manner. For example, a typical cellular network may comprise a plurality of macrocells that are fairly uniform in the coverage areas they support. In the case of 3rd Generation Partnership Project (3GPP) Universal Mobile Telecommunications System (UMTS) networks, each of these macrocells is connected to a Radio Network Controller (RNC). The RNC generally effectuates radio resource management, as well as some mobility management functionality, such as facilitating handover, maintaining device state, and supporting layer 2 data-plane protocols.
There are some exceptions to the uniform deployment paradigm described above, such as picocell and femtocell networks that are deployed in conjunction with an overarching macrocellular network. That is, picocells and femtocells, which may be considered small cellular base stations or access points, connect to a service provider's macrocellular network via broadband connections, allowing the macrocellular network to be extended either for capacity augmentation or for extending the coverage (e.g., indoors). These picocells and femtocells may be deployed in the same frequency channel as the macrocellular network, in which case they are referred to as co-channel deployments, or in a different frequency channel, in which case they are referred to as dedicated channel deployments.
For example, in-building Distributed Antenna Systems (DASs), powered by picocells, are deployed sporadically within the shadow of the macrocellular network. These picocells are typically manually provisioned to connect to the same RNC that is serving the nearby macrocells, thus facilitating mobility in and out of the coverage area of the picocell. In recent years, there has been a rapid growth of consumer femtocells, which are typically standalone entities serving a limited area. Each of these consumer femtocells is typically connected to a femtocell gateway that interfaces the femtocells with the core network of the cellular service provider.
Location based services provide an opportunity for businesses/service providers to customize and deliver services to users based on a physical location of the user. Such services have conventionally relied on access to data that is either not always known with precision, as is the case with Global Positioning Systems (GPS) for indoor environments, or requires specialized hardware, as is the case with near field communications (NFC). Imprecise data results in inadequate location accuracy for these location based services. Deployment of in-building systems, such as the SpiderCloud® Wireless, Inc. Enterprise RAN (E-RAN), changes these considerations.
With regard to local RANs, such as an E-RAN, a primary function of the local RAN is to provide seamless voice and high bandwidth data access to users that have authorized access, and are connected to the local RAN. As a part of this primary function, and in order to continuously optimize the quality of the connection to the user, the local RAN collects a large volume of data from each user connected to the local RAN. This data is a part of the wireless protocols used between the local RAN and the user for access to the system.
While this data collected from the users is not internally processed for a location estimate of the user, it is a function of the location of the user in the system. Thus, it would be advantageous to exploit the collected data by siphoning the data from, e.g., a local RAN, in a time-sensitive fashion. Doing so would enable development and integration with a location engine, such as a third-party location engine, to provide a high accuracy location estimate for users in an indoor environment.